Data models and pipelines-Get started with data modeling, schemas, and databases
Introduction to Course 2
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Helpful resources and tips
Ed: Overcome imposter syndrome
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Course 2 overview
Welcome to module 1
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Data modeling, design patterns, and schemas
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Get the facts with dimensional models
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Dimensional models with star and snowflake schemas
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Design efficient database systems with schemas
Different data types, different databases
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Database comparison checklist
Data models and pipelines-Choose the right database
The shape of the data
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Design useful database schemas
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Four key elements of database schemas
Review a database schema
Data models and pipelines-How data moves
Data pipelines and the ETL process
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Maximize data through the ETL process
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Choose the right tool for the job
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Business intelligence tools and their applications
ETL-specific tools and their applications
Data models and pipelines-Data-processing with Dataflow
Introduction to Dataflow
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Guide to Dataflow
Coding with Python
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Python applications and resources
Data models and pipelines-Organize data in BigQuery
Gather information from stakeholders
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Merge data from multiple sources with BigQuery
Unify data with target tables
Activity Exemplar: Create a target table in BigQuery
Case study: Wayfair - Working with stakeholders to create a pipeline
Data models and pipelines-Review: Data models and pipelines
Wrap-up
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Glossary terms from module 1
Data models and pipelines-[Optional] Review Google Data Analytics Certificate content
[Optional] Review Google Data Analytics Certificate content about data types
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[Optional] Review Google Data Analytics Certificate content about primary and foreign keys
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[Optional] Review Google Data Analytics Certificate content about BigQuery
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[Optional] Review Google Data Analytics Certificate content about SQL best practices
Dynamic database design-Database performance
Welcome to module 2
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Data marts, data lakes, and the ETL process
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ETL versus ELT
The five factors of database performance
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A guide to the five factors of database performance
Optimize database performance
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Indexes, partitions, and other ways to optimize
Activity Exemplar: Partition data and create indexes in BigQuery
Case study: Deloitte - Optimizing outdated database systems
The five factors in action
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Determine the most efficient query
Dynamic database design-Review: Dynamic database design
Wrap-up
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Glossary terms from module 2
Optimize ETL processes-Optimizing pipelines and ETL processes
Welcome to module 3
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The importance of quality testing
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Seven elements of quality testing
Monitor data quality with SQL
Mana: Quality data is useful data
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Optimize ETL processes-Data schema validation
Conformity from source to destination
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Sample data dictionary and data lineage
Check your schema
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Schema-validation checklist
Activity Exemplar: Evaluate a schema using a validation checklist
Optimize ETL processes-Business rules and performance testing
Verify business rules
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Business rules
Database performance testing in an ETL context
Defend against known issues
Burak: Evolving technology
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Case study: FeatureBase, Part 2: Alternative solutions to pipeline systems
Optimize ETL processes-Review: Optimize ETL processes
Wrap-up
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Glossary terms from module 3
Optimize ETL processes-[Optional] Review Google Data Analytics Certificate content
[Optional] Review Google Data Analytics Certificate content about data integrity
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[Optional] Review Google Data Analytics Certificate content about metadata
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Course 2 end-of-course project-Apply your skills to a workplace scenario
Welcome to module 4
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Continue your end-of-course project
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Explore Course 2 end-of-course project scenarios
Course 2 end-of-course project-Cyclistic scenario
Course 2 workplace scenario overview: Cyclistic
Cyclistic datasets
Observe the Cyclistic team in action
Activity Exemplar: Create your target table for Cyclistic
Course 2 end-of-course project-Google Fiber scenario
Course 2 workplace scenario overview: Google Fiber
Google Fiber datasets
[Optional] Merge Google Fiber datasets in Tableau
Activity Exemplar: Create your target table for Google Fiber
Course 2 end-of-course project-End-of-course project wrap-up
Tips for ongoing success with your end-of-course project
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Luis: Tips for interview preparation
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Course 2 end-of-course project-Course review: The Path to Insights: Data Models and Pipelines
Course wrap-up
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Course 2 glossary
Get started on Course 3